
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "165"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 79 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 75 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	0.00%	0.00%	0.00%	0.00%	8.11%	18.92%	0.513221	-0.718160	0.145384	0.163006	0.392285	-1.173446	0.178518	-0.022466	0.7984	0.7995	0.2531	2.721099	2.795507
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.558423	0.053222	-1.144224	-0.695898	1.484627	-0.946020	0.369569	-0.665894	0.7337	0.7333	0.2525	2.177844	2.313764
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.199860	-0.987612	-0.042950	-0.457288	0.361905	-0.291449	-1.967239	-0.061729	0.0284	0.0285	0.0011	nan	nan
2459591	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	-0.538321	0.077207	-0.895548	-0.112354	0.788682	-1.075874	0.176005	-0.929696	0.6806	0.6923	0.3719	1.805058	1.696564
2459590	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	-0.581712	0.023206	-0.490020	-0.539159	2.006114	-0.740477	1.142015	-0.649835	0.6819	0.6900	0.3643	1.946776	1.818958
2459589	digital_ok	0.00%	0.00%	0.00%	0.00%	15.59%	7.53%	-0.942558	0.007581	-0.061003	-0.865054	0.015143	-1.169184	0.116924	-0.654695	0.7265	0.7204	0.2788	1.590646	1.708158
2459588	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.00%	-0.255055	0.375433	-1.162548	-0.551674	-0.312468	-0.928227	-0.383375	-0.415144	0.7737	0.7545	0.2340	2.761504	2.553936
2459587	digital_ok	0.00%	-	-	-	2.80%	0.00%	-1.023572	-0.058616	-1.126965	-0.157006	0.105315	-0.117303	2.120036	-0.824061	nan	nan	nan	0.695608	0.682138
2459586	digital_ok	0.00%	0.00%	0.00%	0.00%	7.03%	0.00%	-0.030312	0.582828	1.647929	1.127807	2.244103	0.627135	-0.873039	-0.062546	0.6698	0.6884	0.3547	1.772185	1.718195
2459585	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	161.795037	162.201929	inf	inf	17.418291	15.506998	1910.966616	1356.027230	nan	nan	nan	0.000000	0.000000
2459584	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.345957	0.817137	0.080399	1.149223	-2.204934	-0.801365	0.678518	-0.640379	0.6229	0.6348	0.3658	1.315963	1.296328
2459583	digital_ok	0.00%	0.00%	0.00%	0.00%	3.23%	0.00%	-0.964067	0.301310	-0.323064	0.973901	1.722944	-0.182136	1.621029	0.083411	0.6822	0.6918	0.3804	1.864091	1.943354
2459582	digital_ok	0.00%	0.00%	0.00%	0.00%	2.70%	0.00%	-0.914856	0.514430	-0.313986	0.983766	1.264823	-0.450103	0.826513	-0.278267	0.6843	0.6943	0.3850	1.754266	1.771368
2459581	digital_ok	0.00%	0.00%	0.00%	0.00%	3.24%	0.00%	-1.033305	0.881980	0.274414	0.561109	0.781637	-0.494904	2.036683	0.001004	0.6850	0.6965	0.3821	1.774222	1.744334
2459580	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	0.00%	-0.947301	0.528881	-0.364930	0.752046	2.053179	-0.529321	-0.357324	-0.642883	0.6857	0.6951	0.3805	1.905128	1.838315
2459579	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	96.24%	-0.163962	1.127305	0.041022	1.432537	1.272471	-0.700449	0.589544	-0.007058	0.6747	0.6842	0.3906	14.433889	18.240487
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-1.081732	-0.760871	-1.008346	-0.663686	0.794342	-0.695456	-0.890118	-0.933177	0.0311	0.0366	0.0006	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	9.73%	11.35%	-0.597943	0.604330	0.185063	0.654477	0.882061	-0.152981	1.706910	0.506605	0.6813	0.6916	0.3830	1.856295	1.773551
2459576	digital_ok	0.00%	0.00%	0.00%	0.00%	4.84%	0.54%	-0.714667	0.808521	0.403862	0.837540	0.024941	-0.097815	1.184933	0.057563	0.7067	0.7164	0.3815	1.552980	1.545846
2459575	digital_ok	0.00%	0.00%	0.00%	0.00%	4.30%	0.00%	-0.759334	0.546772	-0.257181	0.677267	0.551859	-0.010738	-0.103427	0.845405	0.7696	0.7738	0.3204	1.557959	1.525624
2459574	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	-0.426647	1.047636	-0.012761	0.631978	-0.225280	0.091268	0.325837	-0.620988	0.7015	0.7103	0.3789	1.573464	1.492931
2459573	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	-0.589495	1.115757	-0.313844	1.168631	1.326955	-0.136057	1.114424	0.092464	0.6949	0.7078	0.3897	1.652384	1.572796
2459572	digital_ok	100.00%	75.68%	75.68%	0.00%	100.00%	0.00%	-0.275566	0.641099	9.629598	9.866949	207.843152	209.230723	14.207529	13.995316	0.1913	0.2034	0.0787	1.377815	1.403447
2459571	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	-0.895691	-0.973489	-0.491982	-0.695738	109.897104	116.032405	5.628871	5.070194	0.0333	0.0457	0.0010	nan	nan
2459570	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.54%	-1.037468	-1.171457	-1.143922	-1.161735	-0.835781	-0.979420	0.373175	-0.030111	0.6776	0.7032	0.3573	1.691297	1.694777
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	-0.897654	0.749659	0.132776	0.709318	0.300132	0.703290	0.270314	0.759001	0.7854	0.7915	0.2202	2.121081	2.087036
2459566	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	-0.213275	0.654232	0.065810	0.911378	0.788289	-0.079191	2.305901	0.198246	0.6937	0.7090	0.3957	1.733133	1.730146
2459565	digital_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	5.20%	0.00%	-0.360608	-0.126990	0.693308	-0.426979	-0.713352	1.875063	2.940480	0.134836	0.7099	0.7163	0.3968	1.832323	1.764973
2459563	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.649500	0.354827	0.495464	-0.699168	1.900979	-0.655882	2.411611	-0.457120	0.7054	0.7158	0.3895	1.785722	1.833985
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.459812	0.667821	0.345328	0.752296	-0.157782	0.727608	1.412445	0.280745	0.7111	0.7215	0.3835	1.983457	1.796652
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.54%	-0.725129	0.479384	0.000863	0.555523	-0.740197	0.576653	0.517085	0.110191	0.7115	0.7217	0.3832	1.924423	1.814940
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.371156	0.561714	0.287871	0.564958	-0.401247	0.701140	1.050786	0.096502	0.7030	0.7143	0.3994	1.760313	1.692892
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.513769	0.645245	0.336173	0.534910	-0.702816	0.406297	1.287827	0.224286	0.7074	0.7156	0.3830	1.853953	1.729129
2459558	digital_ok	0.00%	0.00%	0.00%	0.00%	13.98%	0.00%	-1.066451	1.166401	-0.552169	1.073135	1.370718	-0.269883	1.594805	-0.010015	0.7070	0.7130	0.3977	1.602153	1.546075
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0294	0.0287	0.0014	nan	nan
2459556	digital_ok	0.00%	0.00%	0.00%	0.00%	5.41%	0.00%	-0.680375	1.375880	-0.312399	1.285094	0.889762	-0.292659	2.611047	-0.336867	0.7049	0.7112	0.3925	1.713524	1.674721
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7123	0.7218	0.3979	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7110	0.7187	0.3966	nan	nan
2459552	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-1.180558	1.287305	0.377055	0.952767	0.277832	-1.119798	1.481621	-0.635868	0.7131	0.7187	0.3982	1.557877	1.639919
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.527695	0.700778	0.377841	0.651991	-0.457085	0.593731	1.624834	0.141415	0.7098	0.7196	0.3893	2.033991	1.795480
2459550	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0467	0.0493	0.0007	nan	nan
2459549	digital_ok	0.00%	0.00%	0.00%	0.00%	8.11%	0.00%	-0.264518	1.288014	0.061665	0.844794	0.690629	-0.646989	0.973627	-0.561137	0.7071	0.7146	0.4000	2.028369	1.814442
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	-1.509017	0.343708	1.171630	-0.792977	-0.000923	1.082228	2.320008	-0.409265	0.7644	0.7642	0.3450	1.879661	1.768236
2459541	digital_ok	0.00%	1.79%	1.79%	0.00%	21.88%	0.00%	-0.922295	0.773812	-0.029003	0.817087	1.716480	0.730294	2.041393	-0.101673	0.7544	0.7516	0.3190	1.575372	1.561735
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	1.34%	-0.679961	0.913781	-0.150999	0.674976	1.823415	0.149077	0.071800	0.458670	0.7677	0.7648	0.3311	1.760571	1.601170
2459536	digital_ok	0.00%	0.00%	0.00%	0.00%	5.93%	0.00%	-0.948488	1.723865	1.645131	1.763878	0.248159	0.602649	0.788353	0.101966	0.7378	0.7405	0.4224	1.980418	1.766443
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	0.00%	-0.433839	0.595546	0.003106	0.606365	1.505489	0.158533	-0.349840	-0.166402	0.7943	0.7862	0.3520	1.922837	1.693056
2459534	digital_ok	100.00%	98.66%	98.66%	0.00%	100.00%	0.00%	3.488727	5.271776	-1.335575	-0.458385	2.416590	4.368025	8.340786	8.295980	0.0811	0.0666	0.0204	1.264910	1.268897
2459533	digital_ok	0.00%	0.00%	0.00%	0.00%	4.89%	0.00%	-1.197328	0.874848	-0.701764	0.778222	1.582935	-0.415925	0.030766	0.016848	0.7343	0.7354	0.4024	1.861266	1.700787
2459532	digital_ok	0.00%	0.00%	0.00%	0.00%	5.00%	5.56%	-1.077770	1.492717	-0.163690	1.798579	1.489149	-0.390708	-0.180596	-0.380284	0.7333	0.7390	0.4052	1.941799	1.828449
2459530	digital_ok	0.00%	0.00%	0.00%	0.00%	4.73%	0.00%	-0.607461	0.478969	0.149523	0.572651	-0.274545	0.873906	0.174679	0.135983	0.7530	0.7503	0.3990	1.961584	1.762140
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.733222	1.448261	-0.582711	1.013301	0.544125	0.361076	-0.526412	-0.435810	0.9109	0.9389	0.2796	0.000000	0.000000
2459523	digital_ok	0.00%	-	-	-	12.24%	0.00%	-1.829616	1.185294	0.631738	1.399279	1.954404	0.126319	-0.115435	0.591064	nan	nan	nan	2.687344	2.303981
2459522	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-1.187436	0.976755	0.215190	1.470070	-0.025899	0.170147	0.053249	0.140911	0.7156	0.7063	0.3079	1.925692	2.358169
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.850306	-0.830038	-1.049409	-1.012842	2.592022	-0.490283	-0.588683	-0.748430	0.0331	0.0324	0.0009	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	-0.792930	1.853259	-1.129711	0.941284	0.059196	0.125208	-0.919679	0.746623	0.9220	0.9545	0.1504	0.000000	0.000000
2459505	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-1.287029	1.288465	0.425335	1.720851	-0.003335	0.396969	0.589539	0.486502	0.0751	0.0652	0.0034	1.343043	1.341068
2459503	digital_ok	0.00%	0.00%	0.00%	0.00%	89.47%	0.00%	-1.070872	2.324958	0.012884	1.309540	1.336687	1.764733	1.368714	1.394472	0.8752	0.8736	0.4205	0.000000	0.000000
2459501	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.810073	1.966014	-0.871234	0.666709	1.323531	0.252228	0.260770	-0.006481	0.1173	0.1108	0.0117	0.000000	0.000000
2459500	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.478486	1.986110	-0.695629	0.295312	-0.925157	0.398237	-0.426234	-0.476804	0.1038	0.0936	0.0095	0.000000	0.000000
2459499	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.555498	2.112342	-1.157798	0.880006	-0.967144	0.628786	-0.049219	-0.240847	0.1188	0.1212	0.0082	0.000000	0.000000
2459498	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.982546	1.406441	-1.086054	0.608879	-0.699086	0.535509	1.727927	1.428555	0.1133	0.1179	0.0132	0.000000	0.000000
2459497	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.920913	1.818448	-1.101076	0.584412	-0.781727	0.102368	1.820646	1.376162	0.1375	0.1417	0.0148	0.000000	0.000000
2459496	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	-0.988917	1.812402	-0.851195	0.794186	-0.677892	0.405670	-0.451576	-0.333699	0.9214	0.9316	0.3227	0.000000	0.000000
2459495	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	-0.816880	1.344674	-0.758693	0.673285	-0.565017	0.171985	-0.116280	-0.067263	0.9178	0.9282	0.3569	0.000000	0.000000
2459494	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	-0.630843	2.131224	-1.043715	0.854879	-1.187950	0.438419	-0.148502	0.093604	0.9011	0.9173	0.3745	0.000000	0.000000
2459492	digital_ok	0.00%	0.00%	0.00%	0.00%	50.00%	0.00%	-1.241421	1.055088	-0.056629	0.389169	2.049058	-0.402149	-0.443423	0.039115	0.7691	0.8162	0.3198	0.405390	0.358523
2459491	digital_ok	0.00%	0.00%	0.00%	0.00%	50.00%	0.00%	-0.425683	0.731185	0.748455	0.997908	1.597489	1.089457	-0.252849	0.823694	0.8070	0.8311	0.2530	0.844775	0.848573
2459490	digital_ok	0.00%	0.00%	0.00%	0.00%	47.37%	0.00%	-0.325393	0.928462	0.846563	0.050306	1.141153	-0.212122	-0.307770	0.252712	0.7592	0.8111	0.3694	1.900943	2.000635
2459489	digital_ok	0.00%	0.00%	0.00%	0.00%	47.37%	0.00%	-1.130860	1.025199	0.335188	0.529073	2.265293	-0.123851	-0.111455	0.538180	0.7772	0.8189	0.3176	1.703965	1.732676
2459488	digital_ok	0.00%	0.00%	0.00%	0.00%	85.71%	0.00%	-1.248994	1.014448	0.018004	0.725611	0.793381	-0.108858	0.315103	0.861857	0.7786	0.8211	0.3499	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 165 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 165, Node 14:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 165: 2459602

Antenna 165: 2459598

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶